stimation of convective scheme parameters in a simplified general circulation model using Local Ensemble Transform Kalman Filter

JUAN RUIZ Y MANUEL PULIDO

Foz do Iguazu

Congreso; American Geophysical Union, Meeting of the Americas 2010; 2010

American Geophysical Union

In this work the Local Ensemble Transform Kalman Filter (LETKF) is used for the estimation of model state and parameters included in the convective parameterization. Two main different implementations of this technique were examined: one is the use of a single ensemble were initial conditions and parameters were perturbed, this will be referred as the simultaneous estimation; the other consist of two ensembles one with perturbations in the model state only and the other with perturbations in the parameters only. The initial condition ensemble uses the most recent estimation of the parameters for all the ensemble members, and the parameter ensemble uses the analysis ensemble mean as the initial condition for the state variables for all the ensemble members, this will be referred as the parallel estimation. The accuracy and convergence of the different approaches were evaluated using experiments where the true evolution of the system is assumed to be known and consists of a long integration of the general circulation model (GCM). Observations were generated from the true evolution of the system, at fixed intervals, assuming a normally distributed random observational error. The assimilation was started from a random state and the analysis cycle was iterated during 3 months using a 6 hours assimilation window. In the parallel implementation, the sensitivity of the parameter estimation to the errors in the state variables is explored and compared with experiments where the state variables are assumed to be perfectly known. It has been found that the LETKF scheme can produce an accurate estimation of those parameters that produce a stronger response in the state variables. The simultaneous estimation of initial conditions and parameters reduces analysis error and improves the short range forecast and is also computationally cheaper. The experiments shows that most parameters can be successfully estimated using LETKF when the initial conditions are perfectly known. However when errors are present in the initial conditions, the estimation loss accuracy for some parameters and the convergence time of the parameters increases. The inclusion of an outer loop in the LETKF scheme helps to reduce convergence time both for the initial conditions and the parameter estimations and helps to deal with non-linearities introduced by the inclusion of the parameter model state.